N - mixture models by species

modelling N by site to get relative abundance

abundance by site will be used as a cov on predator occupancy

7 models evaluated, dot, jdt + jdtSQ, lure + jdt + jdtSQ


Species:      YellowBelliedMarmot



Metadata Summary:

N_sites N_counts N_detections rep_period iterations burnin thin
127 496 167 7 days 120000 20000 10



Detections by Year:

Yr 2016 2017 2018 2019 2020
sites 19 31 19 32 26
detections 13 63 17 50 24
N.dot.model 4 15 10 54 41



WAIC

Models by WAIC:
model description WAIC N.total.est
fm7 counts 13.78466 127
fm4 lure + jdt 825.63785 144
fm6 lure + jdt + jdtSq 830.08352 144
fm2 jdt 841.75801 99
fm5 jdt + jdtSq 846.14263 101
fm1 dot 1096.23128 124
fm3 lure 1117.58635 106



Model summaries:



model: fm1
dot



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
p[1] NA 8149 0.151 0.15 0.08 0.22 0 1.001
p[2] NA 7043 0.163 0.159 0.12 0.20 0 1.001
p[3] NA 3588 0.034 0.031 0.01 0.06 0 1.0009
p[4] NA 1347 0.033 0.031 0.01 0.05 0 1.0009
p[5] NA 704 0.021 0.015 0.00 0.04 0 0.9981
lambda[1] NA 9289 0.238 0.191 0.06 0.41 0 1.0007
lambda[2] NA 9517 0.545 0.5 0.29 0.78 0 1.001
lambda[3] NA 1875 0.905 0.714 0.30 1.46 0 1.001
lambda[4] NA 441 2.042 1.458 0.80 3.15 0 1.0009
lambda[5] NA 219 2.313 0.993 0.37 4.44 0 1.0011
N[93] NA 1262 2.206 1 1.00 4.00 0 1.0006
N[67] NA 3061 3.320 2.994 2.00 5.00 0 1.001
N[94] NA 1186 2.931 2.002 1.00 5.00 0 1
N[6] NA 9301 0.016 err 0.00 0.00 err err
N[7] NA 9323 0.019 err 0.00 0.00 err err

p[1]

p[2]

p[3]

p[4]

p[5]

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[93]

N[67]

N[94]

N[6]

N[7]







model: fm2
jdt



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
alpha jdt 1729 2.131 2.127 1.84 2.43 0 1.001
alpha0 NA 1555 -4.439 -4.426 -4.84 -4.00 0 1.001
lambda[1] NA 6993 0.438 0.326 0.12 0.74 0 1.0008
lambda[2] NA 6067 1.226 1.119 0.68 1.77 0 1.001
lambda[3] NA 5764 0.941 0.775 0.41 1.41 0 1.001
lambda[4] NA 8725 0.968 0.899 0.62 1.31 0 1.001
lambda[5] NA 6517 1.042 1.014 0.57 1.54 0 1.001
N[70] NA 10000 0.940 0 0.00 2.00 1 err
N[115] NA 9429 1.375 1 1.00 2.00 0 1
N[25] NA 8888 0.159 err 0.00 1.00 err err
N[91] NA 8260 1.308 1 1.00 2.00 0 1
N[55] NA 9635 0.307 0 0.00 1.00 1 err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[70]

N[115]

N[25]

N[91]

N[55]

alpha relationship







model: fm3
lure



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
alpha lure 7925 0.111 0.108 -0.03 0.25 0.45582 0.8975
alpha0 NA 2938 -2.947 -2.897 -3.19 -2.66 0 1.001
lambda[1] NA 6706 0.491 0.346 0.13 0.83 0 1.0008
lambda[2] NA 4063 1.618 1.415 0.92 2.31 0 1.001
lambda[3] NA 8753 0.626 0.575 0.28 0.94 0 1.001
lambda[4] NA 4719 1.245 1.143 0.77 1.70 0 1.001
lambda[5] NA 7793 0.672 0.625 0.35 0.98 0 1.001
N[76] NA 8238 2.143 1.999 1.00 3.00 0 1.0006
N[47] NA 7902 2.167 1 1.00 3.00 0 1.0006
N[94] NA 8644 1.954 2.003 1.00 3.00 0 1.0005
N[65] NA 9433 1.323 1 1.00 2.00 0 1
N[41] NA 7868 0.271 err 0.00 1.00 err err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[76]

N[47]

N[94]

N[65]

N[41]

alpha relationship







model: fm4
lure + jdt



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
alpha[1] lureDays 1747 -0.649 -0.659 -0.86 -0.42 0 1.001
alpha[2] julianDt 1008 2.513 2.503 2.18 2.87 0 1.001
alpha0 NA 964 -4.769 -4.728 -5.24 -4.27 0 1.001
lambda[1] NA 9701 0.321 0.228 0.09 0.55 0 1.0008
lambda[2] NA 8080 0.917 0.842 0.50 1.32 0 1.001
lambda[3] NA 7673 0.797 0.71 0.36 1.19 0 1.001
lambda[4] NA 8268 1.018 0.958 0.64 1.37 0 1.001
lambda[5] NA 1453 3.206 2.439 1.23 5.14 0 1.001
N[62] NA 7131 1.192 err 1.00 2.00 err err
N[31] NA 9461 0.014 err 0.00 0.00 err err
N[11] NA 10000 0.054 err 0.00 0.00 err err
N[70] NA 10000 1.012 0 0.00 2.00 1 err
N[61] NA 9658 0.183 err 0.00 1.00 err err

alpha[1]

alpha[2]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[62]

N[31]

N[11]

N[70]

N[61]







model: fm5
jdt + jdtSq



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
alpha[1] julianDt 141 2.997 2.84 1.97 4.16 0 1.001
alpha[2] julianDtSq 156 -0.435 -0.406 -0.96 0.10 0.42233 0.9072
alpha0 NA 158 -4.869 -4.766 -5.51 -4.14 0 1.001
lambda[1] NA 8056 0.423 0.312 0.11 0.71 0 1.0008
lambda[2] NA 5761 1.243 1.117 0.68 1.77 0 1.001
lambda[3] NA 6722 0.915 0.776 0.40 1.37 0 1.001
lambda[4] NA 8269 0.981 0.933 0.62 1.32 0 1.001
lambda[5] NA 5687 1.016 0.951 0.53 1.47 0 1.001
N[51] NA 8846 0.546 0 0.00 2.00 1 err
N[74] NA 8743 0.063 err 0.00 0.00 err err
N[5] NA 8542 1.923 2.002 1.00 3.00 0 1.0005
N[122] NA 7869 2.015 1 1.00 3.00 0 1.0005
N[113] NA 9708 0.353 0 0.00 1.00 1 err

alpha[1]

alpha[2]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[51]

N[74]

N[5]

N[122]

N[113]

julian date relationship







model: fm6
lure + jdt + jdtSq



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
alpha[1] lureDays 1843 -0.647 -0.607 -0.86 -0.41 0 1.001
alpha[2] julianDt 215 2.668 2.522 1.77 3.58 0 1.001
alpha[3] julianDtSq 296 -0.075 -0.045 -0.55 0.36 0.97918 0.5954
alpha0 NA 372 -4.852 -4.756 -5.48 -4.24 0 1.001
lambda[1] NA 9067 0.319 0.228 0.09 0.55 0 1.0007
lambda[2] NA 7735 0.911 0.858 0.49 1.30 0 1.001
lambda[3] NA 7503 0.798 0.707 0.37 1.22 0 1.001
lambda[4] NA 7153 1.022 0.948 0.65 1.39 0 1.001
lambda[5] NA 1409 3.209 2.263 1.24 5.20 0 1.001
N[44] NA 10000 0.908 0 0.00 2.00 1 err
N[29] NA 8185 0.896 0 0.00 2.00 1 err
N[34] NA 10000 0.075 err 0.00 0.00 err err
N[20] NA 10000 0.890 0 0.00 2.00 1 err
N[78] NA 10000 0.997 0 0.00 2.00 1 err

alpha[1]

alpha[2]

alpha[3]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[44]

N[29]

N[34]

N[20]

N[78]







model: fm7
counts



summary table

param covariate iters_ef mean mode hdi_89pct_lower hdi_89pct_upper bayes_P pct.density
alpha counts 7181 13.500 13 11.46 15.50 0 1.001
alpha0 NA 8457 -5.884 -5.739 -6.89 -4.88 0 1.001
lambda[1] NA 7007 0.931 0.642 0.22 1.56 0 1.0008
lambda[2] NA 9303 0.977 0.872 0.51 1.39 0 1.001
lambda[3] NA 10000 0.975 0.872 0.41 1.46 0 1.001
lambda[4] NA 10000 0.998 0.944 0.65 1.34 0 1.001
lambda[5] NA 9483 0.982 0.928 0.51 1.39 0 1.001
N[37] NA 9578 0.970 0 0.00 2.00 1 err
N[53] NA 0 1.000 err 1.00 1.00 err err
N[94] NA 0 1.000 err 1.00 1.00 err err
N[88] NA 10000 0.974 0 0.00 2.00 1 err
N[50] NA 0 1.000 err 1.00 1.00 err err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[37]

N[53]

## Warning in cor(X, use = "pairwise.complete.obs"): the standard deviation is zero
## Warning: Removed 50 rows containing missing values (geom_bar).

N[94]

## Warning in cor(X, use = "pairwise.complete.obs"): the standard deviation is zero
## Warning: Removed 50 rows containing missing values (geom_bar).

N[88]

N[50]

## Warning in cor(X, use = "pairwise.complete.obs"): the standard deviation is zero
## Warning: Removed 50 rows containing missing values (geom_bar).

alpha relationship